Saliency auxiliary objects for visual tracking

Abstract

The background contains potential favorable information for visual tracking. The recent correlation filter-based tracking methods achieve great success by densely sampling at the target and its surrounding background. To further improve the tracking ability against deformation (DEF) and occlusion (OCC), we propose to utilize auxiliary objects. First, auxiliary object candidates with objectness are generated with unsupervised salient region detection. Second, the graph model between the target with all auxiliary object candidates is established when these objects are tracked individually. Third, all auxiliary objects vote for correcting the predicting result of the target tracker. Experimental results show that auxiliary objects are helpful for tracking the target, especially for improving the robustness on DEF and OCC.